This paper presents a deep learning network, called MVPNet and a customized data augmentation technique, called NuView, for magnification independent diagnosis. MVPNet is tailored to tackle the most common issues (diversity, relatively small size of datasets and manifestation of diagnostic biomarkers at various magnification levels) with breast cancer histology data to perform the classification. The network simultaneously analyzes local and global features of a given tissue image. It does so by viewing the tissue at varying levels of relative nuclei sizes. MVPNet has significantly less parameters than standard transfer learning deep models with comparable performance and it combines and processes local and global features simultaneously for effective diagnosis.